Real Life Use Cases for Predictive Analytics

You may have heard someone mention predictive analytics or algorithms as a way of identifying future outcomes based on historical data. While the competitive advantage does exist for most companies that delve into these predictive models, some industries may shrug their shoulders and think it’s not suited to their needs.

The truth is that data analytics can do wonders for just about any business. Here are a few implementations taking place in the working world right now.

1. Manufacturing

When it comes to bracing for future market trends or getting a gauge on the latest developments, manufacturing analytics relies upon predictive analytics. Historically, manufacturers struggled to use all of the data they received from the supply chain to production to customer response.

The manufacturing sector relied on expensive tools to help with any possible prediction or understanding of their workflow. However, new developments in big data analytics allow some companies to get a 360 view of their operations and truly optimize their outputs.

Manufacturing analytics provide:

• Real-time insight.
• Giving decision-makers a competitive edge by improving quality.
• Accelerating innovation.
• Redefining the customer experience.

These data analytics help increase productivity and profitability by using machine learning models to uncover hurdles in project management and potential for expansion. For example, by understanding supplier performance, manufacturers can work on analytics related to order management.

Large amounts of data can also be used to understand transportation and conduct real-time quality monitoring through each manufacturing process step. This abundance of data helps to create an efficient factory, optimizing maintenance and output.

2. Education

More school districts are relying on analytics to give leaders and staff some insight into student performance. With current data, district leaders can monitor progress and identify where to align their resources. For example, teachers can use predictive analytics to improve efforts tailored to individual students, regardless of their level of education.

Indicators for data analytics help to promote control and efficiency within a schooling environment. This use of data analytics can monitor trends in attendance and performance in courses from electives to the most rigorous workloads.

Most universities have relied upon predictive analytics to understand the undergraduate or graduate students they attract to campus. For example, schools that offer a master’s of business administration, or MBA, not only instruct students on business analytics but rely upon their own data management to better understand applicants.

Business schools use this data for strategic thinking to understand tuition affordability for prospective students and how students group by demographic. An MBA program may look at the data to understand student performance, highlighting which foundation courses attract the most students to the school and the best practice to assure their grads stand out in the business world.

3. Healthcare

From inpatient care to pharmaceuticals, the healthcare industry is relying on data analytics more than ever. Perhaps the most significant evidence of this has been brought on by the COVID-19 pandemic, as experts rely on a wide array of data regarding testing, vaccination, and case numbers to make discoveries in how COVID spreads and how to combat it.

Using these analytics has helped direct leaders create a new normal, loosening restrictions in areas that are winning the war against this invisible enemy.

Data analytics also help health insurance companies understand their global market, ranging from customer service requests that may be becoming more prevalent to concerns over business processes that delay handling claims.

Analytics also help hospitals and doctor’s offices better understand patient care, recognizing the wants and needs of those seeking care within their respective systems.